Self-Adaptive Step Firefly Algorithm
In the standard firefly algorithm, each firefly has the same step settings and its values decrease from iteration to iteration. Therefore, it may fall into the local optimum. Furthermore, the decreasing of step is restrained by the maximum of iteration, which has an influence on the convergence spee...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2013/832718 |
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| _version_ | 1832551598728937472 |
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| author | Shuhao Yu Shanlin Yang Shoubao Su |
| author_facet | Shuhao Yu Shanlin Yang Shoubao Su |
| author_sort | Shuhao Yu |
| collection | DOAJ |
| description | In the standard firefly algorithm, each firefly has the same step settings and its values decrease from iteration to iteration. Therefore, it may fall into the local optimum. Furthermore, the decreasing of step is restrained by the maximum of iteration, which has an influence on the convergence speed and precision. In order to avoid falling into the local optimum and reduce the impact of the maximum of iteration, a self-adaptive step firefly algorithm is proposed in the paper. Its core idea is setting the step of each firefly varying with the iteration, according to each firefly’s historical information and current situation. Experiments are made to show the performance of our approach compared with the standard FA, based on sixteen standard testing benchmark functions. The results reveal that our method can prevent the premature convergence and improve the convergence speed and accurateness. |
| format | Article |
| id | doaj-art-3982b827ded6449e92040a09248e3830 |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-3982b827ded6449e92040a09248e38302025-02-03T06:00:59ZengWileyJournal of Applied Mathematics1110-757X1687-00422013-01-01201310.1155/2013/832718832718Self-Adaptive Step Firefly AlgorithmShuhao Yu0Shanlin Yang1Shoubao Su2Institute of Computer Network Systems, Hefei University of Technology, Hefei 230009, ChinaInstitute of Computer Network Systems, Hefei University of Technology, Hefei 230009, ChinaSchool of Information Engineering, West Anhui University, Lu'an 237012, ChinaIn the standard firefly algorithm, each firefly has the same step settings and its values decrease from iteration to iteration. Therefore, it may fall into the local optimum. Furthermore, the decreasing of step is restrained by the maximum of iteration, which has an influence on the convergence speed and precision. In order to avoid falling into the local optimum and reduce the impact of the maximum of iteration, a self-adaptive step firefly algorithm is proposed in the paper. Its core idea is setting the step of each firefly varying with the iteration, according to each firefly’s historical information and current situation. Experiments are made to show the performance of our approach compared with the standard FA, based on sixteen standard testing benchmark functions. The results reveal that our method can prevent the premature convergence and improve the convergence speed and accurateness.http://dx.doi.org/10.1155/2013/832718 |
| spellingShingle | Shuhao Yu Shanlin Yang Shoubao Su Self-Adaptive Step Firefly Algorithm Journal of Applied Mathematics |
| title | Self-Adaptive Step Firefly Algorithm |
| title_full | Self-Adaptive Step Firefly Algorithm |
| title_fullStr | Self-Adaptive Step Firefly Algorithm |
| title_full_unstemmed | Self-Adaptive Step Firefly Algorithm |
| title_short | Self-Adaptive Step Firefly Algorithm |
| title_sort | self adaptive step firefly algorithm |
| url | http://dx.doi.org/10.1155/2013/832718 |
| work_keys_str_mv | AT shuhaoyu selfadaptivestepfireflyalgorithm AT shanlinyang selfadaptivestepfireflyalgorithm AT shoubaosu selfadaptivestepfireflyalgorithm |